IGSNRR OpenIR
Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records
Liu, Zhang1,2; Ma, Ting1,2; Du, Yunyan1,2; Pei, Tao1,2; Yi, Jiawei1,2; Peng, Hui1,2
2018-04-01
Source PublicationTRANSACTIONS IN GIS
ISSN1361-1682
Volume22Issue:2Pages:494-513
Corresponding AuthorMa, Ting(mting@lreis.ac.cn)
AbstractUnderstanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time-series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log-linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub-district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.
DOI10.1111/tgis.12323
WOS KeywordBIG DATA ; BUILDING-LEVEL ; PATTERNS ; DISTRIBUTIONS ; BEHAVIOR ; HOTSPOTS ; NETWORK ; SPACE ; AREAS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[4159840011] ; National Natural Science Foundation of China[41771418] ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China[41421001] ; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[088RA500PA] ; Institute of Geographic Sciences and Natural Resources Research, CAS[2014RC102]
Funding OrganizationNational Natural Science Foundation of China ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; Institute of Geographic Sciences and Natural Resources Research, CAS
WOS Research AreaGeography
WOS SubjectGeography
WOS IDWOS:000430399600007
PublisherWILEY
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57299
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMa, Ting
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Zhang,Ma, Ting,Du, Yunyan,et al. Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records[J]. TRANSACTIONS IN GIS,2018,22(2):494-513.
APA Liu, Zhang,Ma, Ting,Du, Yunyan,Pei, Tao,Yi, Jiawei,&Peng, Hui.(2018).Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records.TRANSACTIONS IN GIS,22(2),494-513.
MLA Liu, Zhang,et al."Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records".TRANSACTIONS IN GIS 22.2(2018):494-513.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Zhang]'s Articles
[Ma, Ting]'s Articles
[Du, Yunyan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Zhang]'s Articles
[Ma, Ting]'s Articles
[Du, Yunyan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Zhang]'s Articles
[Ma, Ting]'s Articles
[Du, Yunyan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.